Workshops

• Workshop 1. Some Selected Topics on Analysis of Longitudinal Survey Data CANCELLED
• Workshop 2. Over 15 Years of Longitudinal Surveys at Statistics Canada: Lessons and Innovations FULL
• Workshop 3. Multilevel Modeling of Longitudinal Data
FULL

Two full-day workshops will take place on Tuesday October 27th. Simultaneous translation will be provided.

Please note that the number of seats for each workshop is limited

Workshop 1. Some Selected Topics on Analysis of Longitudinal Survey Data CANCELLED

Those already registered for this workshop will be contacted shortly by the Symposium registrar. Our apologies for the inconvenience.

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Workshop 2. Over 15 Years of Longitudinal Surveys at Statistics Canada: Lessons and Innovations FULL

Michelle Simard and François Brisebois from Statistics Canada
(French presentation with simultaneous translation and with French and English materials)

Longitudinal surveys are relatively recent at Statistics Canada. In fact, it wasn’t until the mid-1990s that we saw development of major projects such as the National Longitudinal Survey of Children and Youth (NLSCY), the National Population Health Survey (NPHS) and the Survey of Labour and Income Dynamics (SLID), three surveys that are still active today. Each survey has particular objectives. The NLSCY tries to identify the factors influencing the development of Canadians, from birth to adulthood. The NPHS collects information related to the health of the Canadian population and related socio-demographic information. SLID looks at the economic well-being of Canadians.

At the turn of the current century, Statistics Canada developed three new longitudinal surveys: The Youth in Transition Survey (YITS), the Longitudinal Survey of Immigrants to Canada (LSIC), and the Workplace and Employee Survey (WES) which all focus on specific populations. LSIC studies how new immigrants adapt to Canadian society, in addition to identifying the factors that support, as well as hinder, efforts to integrate. YITS has as its main objective documenting the transition of young adults between school and the labour market. WES explores a wide range of topics related to labour (employers and employees) to determine how employers and their personnel react and adapt to changes in a technologies based competitive environment.

This workshop reviews Statistics Canada’s six longitudinal surveys and puts the emphasis on lessons the organization has learned over the years. It also looks at innovations arising from the design of these surveys and their challenges.

The day begins with a review of the basic principles of longitudinal surveys and the main difficulties involved. Next, individual descriptions will be discussed for the six surveys: a summary of the objectives and the sampling frame, followed by a detailed presentation of the lessons and innovations for each survey.

Each of these surveys is distinct due to its mandate and its methodology. The themes studied during the day will cover various steps in survey methodology, and therefore, should meet the needs of all participants.

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Workshop 3. Multilevel Modeling of Longitudinal Data FULL

Sophia Rabe-Hesketh from Graduate School of Education, University of California, Berkeley and Anders Skrondal from the Division of Epidemiology, Norwegian Institute of Public Health
(English presentation with simultaneous translation and with French and English materials)

In longitudinal studies the same individuals provide responses at several occasions or panel waves. If each person-occasion combination is viewed as a unit of observation, it is straightforward to regress the corresponding response variable on both time-varying and time-constant covariates. However, even after controlling for covariates, some unobserved between-person heterogeneity typically remains, leading to within-person dependence. This unobserved heterogeneity can be accommodated by including person-specific intercepts and possibly person-specific regression coefficients in the model. Such an approach can easily be extended to handle further levels of nesting, for instance individuals nested in schools.

We start by discussing random effects (or multilevel) and fixed effects approaches for different types of responses including continuous, dichotomous, ordinal and counts. These models allow investigation of mean growth curves as well as between-person variability in different aspects of the growth trajectories. Fixed and random effects models can be viewed as conditional or person-specific models. Such models are compared with marginal or population averaged models. Different methods for accommodating or modelling within-subject dependence in the marginal approach are outlined and the distinction between conditional and marginal effects is discussed. Finally, we consider the incorporation of sampling weights in complex survey data.